An examination of TNM staging of melanoma by a machine learning algorithm

Dengyuan Wu, Charles Yang, Stephen Wong, Jon Meyerle, Bowu Zhang, Dechang Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Accurate estimation of mortality in patients with cancer is important when discussing prognosis and selecting treatment. Survival estimation for many cancers is based on Tumor-Node-Metastasis (TNM) staging systems that involve three factors: tumor extent, lymph node involvement, and metastasis. The most recent clinical staging of melanoma uses TNM staging but does not include a growing number of other prognostic features. The Ensemble Algorithm of Clustering of Cancer Data (EACCD) by Chen et al. is a machine learning algorithm that regroups patients with different prognostic factors according to the survival dissimilarity. This algorithm has the potential to integrate emerging prognostic factors to more accurately stage melanoma. In this study, we use EACCD to examine the current AJCC staging of melanoma by analyzing a melanoma dataset from the National Cancer Centers Surveillance, Epidemiology, and End Rresults (SEER) database. Our results demonstrates that the EACCD algorithm generates results in-line with AJCC staging and may provide a mechanism to incorporate other prognostic factors to produce a more nuanced estimation of prognosis and survival.

Original languageEnglish (US)
Title of host publicationICCH 2012 Proceedings - International Conference on Computerized Healthcare
PublisherIEEE Computer Society
Pages120-126
Number of pages7
ISBN (Print)9781467351294
DOIs
StatePublished - Jan 1 2012
Event2012 International Conference on Computerized Healthcare, ICCH 2012 - Hong Kong, China
Duration: Dec 17 2012Dec 18 2012

Publication series

NameICCH 2012 Proceedings - International Conference on Computerized Healthcare

Other

Other2012 International Conference on Computerized Healthcare, ICCH 2012
CountryChina
CityHong Kong
Period12/17/1212/18/12

Keywords

  • Clustering
  • Melanoma
  • Prediction
  • Survival Function
  • TNM

ASJC Scopus subject areas

  • Health Informatics

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